六足機器人仿生CPG自適應步態產生器

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：40

、訪客IP：18.117.183.150

姓名

許勝彥(Sheng-Yen Hsu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

六足機器人仿生CPG自適應步態產生器
(CPG-inspired Control Strategies for Adaptive Locomotion of Hexapod Robot)

相關論文

★ 影像處理運用於家庭防盜保全之研究	★ 適用區域範圍之指紋辨識系統設計與實現
★ 頭部姿勢辨識應用於游標與機器人之控制	★ 應用快速擴展隨機樹和人工魚群演算法及危險度於路徑規劃
★ 智慧型機器人定位與控制之研究	★ 基於人工蜂群演算法之物件追蹤研究
★ 即時人臉偵測、姿態辨識與追蹤系統實現於複雜環境	★ 基於環型對稱賈柏濾波器及SVM之人臉識別系統
★ 改良凝聚式階層演算法及改良色彩空間影像技術於無線監控自走車之路徑追蹤	★ 模糊類神經網路於六足機器人沿牆控制與步態動作及姿態平衡之應用
★ 四軸飛行器之偵測應用及其無線充電系統之探討	★ 結合白區塊視網膜皮層理論與改良暗通道先驗之單張影像除霧
★ 基於深度神經網路的手勢辨識研究	★ 人體姿勢矯正項鍊配載影像辨識自動校準及手機接收警告系統
★ 模糊控制與灰色預測應用於隧道型機械手臂之分析	★ 模糊滑動模態控制器之設計及應用於非線性系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究主要精神是仿效生物對規律性動作的控制機制,其運作是由一群低階神經細胞經互相傳遞及影響,產生規律的周期訊號,再藉由外界刺激與大腦刺激修正該訊號,共同合成為最後運動的型式,此即為中樞型態產生器 (Central Pattern Generator)。本系統將以 Matsuoka′s 神經振盪器作為控制六足機器人動作的 CPG(Central Pattern Generator) 基本組成單元,並搭配三軸加速度計與三軸陀螺儀即時獲取機器人姿態,利用此姿態資訊當作CPG 的回授訊號,使其在中度不規則地形能夠回復水平姿態。仿 CPG 模型控制器為一種分散式控制方法,每個足部的控制器由一群神經振盪器與感受單元所組成,每個足部的控制器彼此互相耦合,藉由不同的耦合方式產生出不同的運動步態。整體控制架構經由個別足部方向的傾角作為回授訊號輸入到神經振盪器改變其振幅大小,再與固定振幅的參考振盪器做比較產生出能平衡身軀的足部高度參考訊號,隨後將此控制訊號經由軌跡產生器轉換為機器人足部動作之軌跡,此軌跡再經由逆運動學得到實際的伺服馬達轉動角度以控制馬達轉動角度,進而達到行進時同時恢復平衡的效果。

摘要(英)

In this thesis, we built a controller that can produce rhythmic locomotion inspired by biology concepts of CPG(Central Pattern Generator). CPG, a group of lower-level neural oscillators coupled each other in the creature’s central system, can produce coordinated oscillatory signals for rhythmic locomotion. The oscillatory signals can be modulated by sensory stimulator or higher-level input. We used the Matsuoka′s neural oscillator to construct CPG-based controller of the Hexapod walking robot for generating feet control signals. The body attitude, used for feedback pathway, can be captured by fusing IMU’s data from 3-axis accelerometer and 3-axis gyroscope. Hexapod robot can be up to horizontal when walking on terrains of medium degrees of irregularity by using the CPG as a controller and body attitude as feedback. The CPG is a distributed control method. Each foot has their own CPG controller construct by a group of neurons and sensory feedback. The different output pattern can be produced by different topology of the CPG. The tilt of each foot’s direction is used as feedback for the external neural oscillator to change oscillation amplitude. Then produce the control signal by comparing the external neural oscillator and reference neural oscillator. Inverse kinematics is used for converting the control signal to produced motor angle command. The cooperation of each part of this system achieved to make hexapod robot walking with tilt recoverability.

關鍵字(中)

★ 六足機器人
★ 神經振盪器
★ 中樞型態產生器
★ 步態設計
★ 加速度計
★ 陀螺儀

關鍵字(英)

★ Hexapod Robot
★ Neural Oscillator
★ Walking Pattern Design
★ Central Pattern Generator
★ Accelerometer
★ Gyroscope

論文目次

摘要.......................................i
ABSTRACT......................................ii
誌謝.............................................iii
目錄................................................iv
圖目錄................................vi
表目錄.....................................ix
第一章緒論................................................1
1.1 研究背景與動機....................................1
1.2 研究成果與貢獻...................................3
1.3 論文架構...............................................4
第二章相關研究與知識背景....................5
2.1 非線性振盪器.......................................5
2.2 神經振盪器...........................................6
2.3 CPG使用在機器人................................9
2.4 歸納.......................................................14
第三章硬體架構設計................................16
3.1 硬體架構概觀.......................................16
3.2 足部機構...............................................16
3.3 主控制板...............................................17
3.3.1 PIC32MX440微控制器........................18
3.3.2 MPU6050姿態感測器.........................19
3.4 電源板.......................................19
3.4.1 TL494交換式變壓控制器...................20
3.4.2 LM7805&LM1117線性變壓器.............21
第四章系統架構設計................................22
4.1 系統架構概觀........................................22
4.2 Matsuoka′s神經振盪器...........................23
4.2.1 振幅可調特性.....................................26
4.2.2 頻率可調特性.....................................27
4.2.3 週期訊號耦合特性.............................28
4.2.4 步態參考產生器設計.........................29
4.3 逆運動學..................................30
4.4 訊號融合濾波器....................................31
4.4.1 低通濾波器.........................................33
4.4.2 高通濾波器.........................................34
4.4.3 互補濾波器.........................................36
4.5 回授設計....................................37
4.6 步態設計..............................39
第五章實驗結果與討論.............................46
5.1 平坦地形前進........................................46
5.2 原地踏步單足踩踏障礙物....................47
5.3 障礙地形前進........................................48
5.4 討論..........................................48
第六章結論與未來發展.............................51
6.1 結論.........................................51
6.2 未來發展..........................52
參考文獻...............................53
附錄.........................................55
文章發表...............................................57

參考文獻

[1] AL Hodgkin, AF Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve," The Journal of physiology, vol. 117, pp.500-544, 1952.
[2] R. FitzHugh, Impulses and physiological states in models of nerve membrane. Biophys. J., 1: 445-466, 1961.
[3] J. Nagumo, S. Arimoto, and S. Yoshizawa, An active pulse transmission line simulating nerve axon. Proc. of IRE, 50: 2061-2070, 1962.
[4] K. Matsuoka, "Sustained oscillations generated by mutually inhibiting neurons with adaption," Biological Cybernetics, vol. 52, pp. 367–376, 1985.
[5] K. Matsuoka, "Mechanisms of frequency and pattern control in the neural rhythm generators,” Biological Cybernetics, vol. 56, pp. 345–353, 1987.
[6] K. Matsuoka, "Analysis of a neural oscillator," Biological Cybernetics, vol. 104, no. 4/5, pp. 297–304, May 2011.
[7] http://matsuoka1.jimdo.com/
[8] D. Belter, P. Łabȩcki, and P. Skrzypczynski, "Map-based adaptive foothold planning for unstructured terrain walking," in Proc. IEEE Int. Conf. on Robot. and Automat., 2010, pp. 5256–5261.
[9] W. A. Lewinger, and R. D. Quinn, "A hexapod walks over irregular terrain using a controller adapted from an insect′s nervous system," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2010, pp.3386 –3391.
[10] A. J. Ijspeert, A.Crespi, and J. M. Cabelguen, "Simulation and robotics studies of salamander locomotion: Applying neurobiological principles to the control of locomotion in robots," NeuroInformatics, vol. 3, num. 3, pp. 171–196, 2005.
[11] M. Williamson, "Neural control of rhythmic arm movements, " Neural Networks., vol. 11, pp. 1379-1394, 1998.
[12] Z. Lu, S. Ma, B. Li, and Y. Wang, "Serpentine locomotion of a snake-like robot controlled by cyclic inhibitory CPG model," in Proc. of IEEE/RSJ Conf. on Intelligent Robots and Systems (IROS 2005), Aug. 2005, pp. 96–101.
[13] Z. Lu, S. Ma, B. Li, and Y. Wang, "3D locomotion of a snake-like robot controlled by cyclic inhibitory CPG model," in Proc. of the IEEE/RSJ Conf. on Intelligent Robots and Systems, Oct. 2006, pp. 3897–3902.
[14] X. Wu, and S. Ma, "CPG-based control of serpentine locomotion of a snake-like robot," Mechatronics, vol. 20(2), pp. 326–334, 2010.
[15] X. Wu, and S. Ma, "Adaptive creeping locomotion of a CPG-controlled snake-like robot to environment change," Autonomous Robots, vol. 28(3), pp. 283–294, 2010.
[16] K. Inoue, T. Sumi, and S. Ma, "CPG-based control of a simulated snake-like robot adaptable to changing ground friction," in Proc. IEEE Int. Conf. on Intelligent Robots and Systems (IROS 2007), Oct. 2007, pp. 1957–1962.
[17] M. Matsubara, J. Nakashini, M. Sato, and K. Doya, "Learning CPGbased biped locomotion with a policy gradient method," Robotics and Autonomous Systems, vol. 54, pp. 911–920, 2006.
[18] Y. Fukuoka , H. Kimura, and A. Cohen, "Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts," Int. J. Robot. Res., vol. 22, no. 3, pp.187–202, 2003.
[19] G. Endo, J. Nakanishi, J. Morimoto, and G. Cheng, "Experimental Studies of a Neural Oscillator for Biped Locomotion with QRIO," in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2005, pp. 598–604.
[20] C. Liu, Q. Chen, and D. Wang, “CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots,” IEEE Trans. on Syst., Man, Cybern. B, Cybern., vol. 41, no. 3, pp. 867–880, Jun. 2011.
[21] C. Liu, and Q. Chen, "Walking control strategy for biped robots based on central pattern generator," in Proc. IEEE Int. Conf. on Robotics and Automation(ICRA), 2012, pp. 57–62.
[22] C. Liu, D. Wang, and Q. Chen, "Central Pattern Generator Inspired Control for Adaptive Walking of Biped Robots," IEEE Trans. on Syst., Man, and Cybern.: Systems, vol. 43, Issue: 5, pp. 1206–1215, Sept. 2013.
[23] L. Xu, W. Liu, Z. Wang, and W. Xu, “Gait Planning Method of a Hexapod Robot based on the Central Pattern Generators: Simulation and Experiment,” in proc. IEEE Int. Conf. on Robotics and Biomimetics, Dec. 2013, pp. 698–703.
[24] H. Yu, W. Guo, J. Deng, M. Li, and H. Cai, “A CPG-based locomotion control architecture for hexapod robot,” in proc. IEEE/RSJ Int. Conf. on Intelligent Robots and systems(IROS), Nov. 2013, pp. 5615–5621.
[25] W. Chen, G. Ren, J. Zhang, and J. Wang, “Smooth transition between different gaits of a hexapod robot via a central pattern generators algorithm,” Journal of Intelligent & Robotic Systems, Vol. 67, Issue: 3-4, pp. 255–270, Sep. 2012.
[26] S. Gay, S. Dégallier, U. Pattacini, A. Ijspeert, and J. S. Victor, “Integration of vision and central pattern generator based locomotion for path planning of a non-holonomic crawling humanoid robot” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., Taiwan, Oct. 2010, pp. 183–189.

指導教授

鍾鴻源(Hung-Yuan Chung)

審核日期

2014-8-15

推文